Book Image

Building Machine Learning Systems with Python

Book Image

Building Machine Learning Systems with Python

Overview of this book

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail. Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques. Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on. Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.
Table of Contents (20 chapters)
Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Creating our first classifier and tuning it


The Naive Bayes classifiers reside in the sklearn.naive_bayes package. There are different kinds of Naive Bayes classifiers:

  • GaussianNB: This assumes the features to be normally distributed (Gaussian). One use case for it could be the classification of sex according to the given height and width of a person. In our case, we are given tweet texts from which we extract word counts. These are clearly not Gaussian distributed.

  • MultinomialNB: This assumes the features to be occurrence counts, which is relevant to us since we will be using word counts in the tweets as features. In practice, this classifier also works well with TF-IDF vectors.

  • BernoulliNB: This is similar to MultinomialNB, but more suited when using binary word occurrences and not word counts.

As we will mainly look at the word occurrences, for our purpose, MultinomialNB is best suited.

Solving an easy problem first

As we have seen when we looked at our tweet data, the tweets are not just...